Systems and methods for personifying communications

ABSTRACT

Systems and methods are described for personifying communications. According to at least one embodiment, the computer-implemented method for personifying a natural-language communication includes observing a linguistic pattern of a user. The method may also include analyzing the linguistic pattern of the user and adapting the natural-language communication based at least in part on the analyzed linguistic pattern of the user. In some embodiments, observing the linguistic pattern of the user may include receiving data indicative of the linguistic pattern of the user. The data may be one of verbal data or written data. Written data may include at least one of a text message, email, social media post, or computer-readable note. Verbal data may include at least one of a recorded telephone conversation, voice command, or voice message.

CROSS REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/489,414 entitled “SYSTEMS AND METHODS FOR PERSONIFYINGCOMMUNICATIONS,” which was filed Apr. 17, 2017, which is a continuationof U.S. patent application Ser. No. 14/607,887 entitled “SYSTEMS ANDMETHODS FOR PERSONIFYING COMMUNICATIONS,” which was filed on Jan. 28,2015 and claims priority from U.S. Provisional Patent Application No.61/934,180 entitled “SYSTEMS AND METHODS FOR PERSONIFYINGCOMMUNICATIONS,” which was filed 31 Jan. 2014, and assigned to theassignee hereof.

BACKGROUND

Advancements in media delivery systems and media-related technologiescontinue to increase at a rapid pace. Increasing demand for media hasinfluenced the advances made to media-related technologies. Computersystems have increasingly become an integral part of the media-relatedtechnologies. Computer systems may be used to carry out severalmedia-related functions. The wide-spread access to media has beenaccelerated by the increased use of computer networks, including theInternet and cloud networking.

Many homes and businesses use one or more computer networks to generate,deliver, and receive data and information between the various computersconnected to computer networks. Users of computer technologies continueto demand increased access to information and an increase in theefficiency of these technologies. Improving the efficiency of computertechnologies is desirable to those who use and rely on computers.

With the wide-spread use of computers and mobile devices has come anincreased presence of home automation and security products.Advancements in mobile devices allow users to monitor and/or control anaspect of a home or business automation system. Communication exchangeswith the automation system can be artificial and unrefined and can, insome instances, cause confusion.

SUMMARY

According to at least one embodiment, the computer-implemented methodfor personifying a natural-language communication includes observing alinguistic pattern of a user. The method may also include analyzing thelinguistic pattern of the user and adapting the natural-languagecommunication based at least in part on the analyzed linguistic patternof the user.

In some embodiments, observing the linguistic pattern of the user mayinclude receiving data indicative of the linguistic pattern of the user.The data may be one of verbal data or written data. Written data mayinclude at least one of a text message, email, social media post, orcomputer-readable note. Verbal data may include at least one of arecorded telephone conversation, voice command, or voice message.

In some embodiments, the method may further compare the written data andthe verbal data and identify differences between the written data andthe verbal data. In some instances, the method may also identify ageographical location of the user indicative of a geographical regionand adapt the natural-language communication to use colloquialismsassociated with the geographical region.

In further embodiments, when analyzing the linguistic pattern of theuser, the method may also identify words or phrases preferred by theuser and identify a preferred sentence structure of the user. In someembodiments, the words or phrases may include slang words orcolloquialisms. In some instances, the method may identify ageographical region associated with the colloquialisms and adapt thenatural-language communication to use colloquialisms associated with thegeographical region. In further examples, the method may identify an agegroup associated with the slang words and colloquialisms and adapt thenatural-language communication to use slang words and colloquialismsassociated with the age group.

In some embodiments, the method may generate a message to deliver to theuser. In some instances, the message may communicate an event within anautomation system.

In some embodiments, adapting the natural-language communication mayinclude updating an algorithm based at least in part on the analyzedlinguistic pattern of the user. The algorithm may generate a message todeliver to the user.

According to another embodiment, an apparatus for personifying anatural-language communication is also described. The apparatus mayinclude a processor, a memory in electronic communication with theprocessor and instructions stored on the memory of the processor. Theprocessor may execute the instructions to observe a linguistic patternof a user, analyze the linguistic pattern of the user, and adapt thenatural-language communication based at least in part on the analyzedlinguistic pattern of the user.

In some embodiments, the apparatus may be associated with an automationsystem. The natural-language communication may be adaptive to individualusers of the automation system. In some instances, the natural-languagecommunication may convey an event at the automation system.

According to another embodiment, a computer-program product forpersonifying a natural-language communication is described. Thecomputer-program product may include a non-transitory computer-readablemedium that may store instructions executable by a processor. Theinstructions may observe a linguistic pattern of a user, analyze thelinguistic pattern of the user, and adapt the natural-languagecommunication based at least in part on the analyzed linguistic patternof the user. In some embodiments, the computer-program product may beassociated with an automation system and the natural-languagecommunication may convey an event at the automation system. In furtherembodiments, the natural-language communication may be one of a textmessage, ping message, email, or voice message.

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described hereinafter. The conceptionand specific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the spirit and scope of the appended claims. Features whichare believed to be characteristic of the concepts disclosed herein, bothas to their organization and method of operation, together withassociated advantages will be better understood from the followingdescription when considered in connection with the accompanying figures.Each of the figures is provided for the purpose of illustration anddescription only, and not as a definition of the limits of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the embodimentsmay be realized by reference to the following drawings. In the appendedfigures, similar components or features may have the same referencelabel. Further, various components of the same type may be distinguishedby following the reference label by a dash and a second label thatdistinguishes among the similar components. If only the first referencelabel is used in the specification, the description is applicable to anyone of the similar components having the same first reference labelirrespective of the second reference label.

FIG. 1 is a block diagram of an environment in which the present systemsand methods may be implemented;

FIG. 2 is a block diagram of another environment in which the presentsystems and methods may be implemented;

FIG. 3 is a block diagram of an example first communication module ofthe environments shown in FIGS. 1 and 2;

FIG. 4 is a block diagram of an example second communication module ofthe environment shown in FIG. 3;

FIG. 5 is a flow diagram illustrating a method for personifying anatural-language communication;

FIG. 6 is a flow diagram illustrating another method for personifying anatural-language communication; and

FIG. 7 is a block diagram of a computer system suitable for implementingthe present systems and methods of FIGS. 1 and 2.

While the embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION

The systems and methods described herein relate to home automation andhome security, and related security systems and automation for use incommercial, residential, and business settings. More specifically, thesystems and methods described herein relate to improved systems andmethods of communicating with users of an automation system. Thecommunication system may enable more personal, natural-languagecommunication between the user and the automation system.

FIG. 1 is a block diagram illustrating one embodiment of an environment100 in which the present systems and methods may be implemented. In someembodiments, the systems and methods described herein may be performedat least in part on or using a device 105. The environment 100 mayinclude the device 105, a user interface 110, and a first communicationmodule 115.

The first communication module 115 may be configured to communicate witha user of the device 105 using natural-language communication. Forexample, communicating with the user of the device 105 may includepersonifying the natural-language communication. This may involve thefirst communication module 115 adapting the communication to mimic alinguistic pattern of the user. Adapting to the linguistic style of theuser may allow the first communication module 115 to readily conveyinformation to the user. The information may communicate an event orinteraction within an automation system. The information may further beconveyed as written or verbal message. The user interface 110 mayfacilitate communication between the user and the device 105.

Referring now to FIG. 2, in some embodiments, an environment 200 mayinclude the components of the environment 100 described above, and mayfurther include an automation system 205, a network 210, a servicestation 215, and a database 220. Additionally, the environment 200 mayinclude a device 105-a, which may be one example of the device 105described above with reference to FIG. 1. The device 105-a mayadditionally include an application 225, a display 230, a microphone235, and a speaker 240. The device 105-a may include various componentsand functionalities that work cooperatively with the user interface 110and the first communication module 115, and/or may operate independentlyof the user interface 110 and the first communication module 115.

In some embodiments, the device 105-a may include one or moreprocessors, one or more memory devices, and/or a storage device.Examples of the device 105-a may include mobile phones, smart phones,tablets, personal digital assistants (PDAs), wearable computers,ultra-mobile PCs, etc. Further examples of the device 105-a may includea viewing device associated with a media content set top box, satelliteset top box, cable set top box, DVRs, personal video recorders (PVRs),personal computing devices, computers, servers, etc. Additionally oralternatively, the device 105-a may represent an automation controllerwhich may be one of a wall-mounted controller, a remote control, a voiceactivated controller, or the like. In some embodiments, the device 105-amay represent a single device or multiple devices.

In some embodiments, the device 105-a may connect directly to theautomation system 205. In further embodiments, the device 105-a mayconnect to the automation system 205 over the network 210. In someembodiments, the device 105-a may control aspects of a monitoredproperty as well as to receive notifications regarding monitoredactivity of the property. The notifications may be written notificationsor verbal messages. For example, the notifications may be a textmessage, email, ping message, voice message, voicemail, phone call, orthe like.

In some embodiments, the automation system 205 may include a sensor 245.The sensor 245 shown in FIG. 2 may represent one or more separatesensors or a combination of two or more sensors in a single sensordevice. For example, the sensor 245 may represent one or more camerasensors and one or more motion sensors connected to environment 200.Additionally, or alternatively, the sensor 245 may represent acombination sensor such as both a camera sensor and a motion sensorintegrated in the same sensor device. Although the sensor 245 isdepicted as connecting directly to automation system 205, in someembodiments, the sensor 245 may connect to the automation system 205over the network 210. Additionally, or alternatively, the sensor 245 maybe integrated with a home appliance or fixture such as a light bulbfixture. The sensor 245 may include an accelerometer to enable thesensor 245 to detect movement. The sensor 245 may include a wirelesscommunication device enabling the sensor 245 to send and receive dataand/or information to and from one or more devices in the environment200. Additionally, or alternatively, the sensor 245 may include a GPSsensor to enable the sensor 245 to track a location of the sensor 245.The sensor 245 may include a proximity sensor to enable the sensor 245to detect proximity of a person relative to a predetermined distancefrom a dwelling (e.g., geo-fencing). The sensor 245 may include one ormore security detection sensors such as, for example, a glass breaksensor, a motion detection sensor, or both. Additionally, oralternatively, the sensor 245 may include a smoke detection sensor, acarbon monoxide sensor, or both.

In some embodiments, the application 225 may allow a user to control anaspect of the monitored property, including security, energy management,locking or unlocking a door, checking the status of a door, locating aperson or an item, controlling lighting, thermostats, cameras, receivingnotification regarding a current status or an anomaly associated with ahome, an office, a place of business, and the like. In someconfigurations, the application 225 may enable the automation system 205to interface with the device 105-a and enable the user interface 110 tocommunicate automation, security, and/or user management content via thedisplay 230 or the speaker 240.

In some embodiments, the device 105-a may communicate with theautomation system 205 and the service station 215 via the network 210.Examples of the network 210 include cloud networks, local area networks(LAN), wide area networks (WAN), virtual private networks (VPN),wireless networks (using 802.11, for example), and/or cellular networks(using 3G and/or LTE, for example), etc. The network 210 may be a singlenetwork, or may include multiple interconnected, overlapping, orcoincidental networks. For example, in some embodiments, the network 210may include multiple networks interconnected to facilitate communicationor may include redundant networks. For example, the network 210 mayrepresent a first network (e.g., the Internet) and a second network(e.g., cellular networks).

The service station 215 shown in FIG. 2 may represent one or moreseparate service stations or a combination of service stations. Theservice station 215 may be a network operations center, a monitoringcenter, a service station or any similar station in association with aprovider of the automation system. In some embodiments, the servicestation 215 may include a second communication module 250. The secondcommunication module 250 may communicate events, occurrences, or thelike associated with the automation system 205.

In some embodiments, the service station 215 may be coupled to thedatabase 220. The database 220 may include, for example, linguisticpatterns for individual users associated with each automation system 205monitored by the service station 215. For example, the database 220 mayinclude a linguistic pattern module 255 which may store the linguisticpatterns of users associated with each automation system 205. Thedatabase 220 may include other information including, for example,historical information about the automation system 205 and other aspectsof the environment 200, contact information for various responsepersonnel, and the like.

FIG. 3 is a block diagram illustrating one example of a firstcommunication module 115-a. The first communication module 115-a may beone example of the first communication module 115 depicted in FIGS. 1and/or 2. As depicted, the first communication module 115-a may includea data collection module 300, a data analysis module 305, a linguisticalgorithm module 310, and a message module 315. The first communicationmodule 115-a may include additional modules and capabilities in otherembodiments. Similarly, the first communication module 115-a may includea fewer number of modules and functionalities than that which isdescribed with reference to FIG. 3. The first communication module 115-amay personify natural-language communications. For example, the firstcommunication module 115-a may adapt communications sent to a user tocontain a natural-language style. The first communication module mayfurther adapt the natural-language message based at least on alinguistic pattern of the user.

In some embodiments, the data collection module 300 may collect variouslinguistic data. For example, the data collection module 300 may collectverbal data or written data associated with a user. The data may beindicative of a linguistic pattern of the user. The verbal data mayinclude at least one of a recorded telephone conversation, voicecommand, voice message, or the like. For example, the user may be usinga device (e.g. device 105) with a user interface (e.g., user interface110). For example, the user interface may allow the data collectionmodule 300 to gather recorded voice data. In some instances, the devicemay include a microphone (e.g., microphone 235) capable of collectingthe user's speech. The data collection module 300 may use the microphoneto record the user's speech.

In other embodiments, the data collection module 300 may collect writtendata indicative of a linguistic pattern associated with the user.Written data may include at least one of a text message, email, socialmedia post, computer-readable note, or the like. In some embodiments,the data collection module 300 may use a device (e.g. device 105)associated with the user to gather information. For example, the devicemay have a user interface associated with a keyboard for entering text(i.e. written data). The data collection module 300 may gather thewritten data.

In some embodiments, the data analysis module 305 may analyze the datacollected by the data collection module 300. For example, the dataanalysis module 305 may identify the verbal and written linguisticspatterns of the user. The linguistic patterns may include the form,meaning, and context of language. In some embodiments, linguisticpatterns may include sentence structure, colloquialisms, phrases, speechpatterns, writing patterns, writing style, grammar habits, a user'slexicon, discourse, dialect, or the like.

In some embodiments, the data analysis module 305 may differentiate andidentify the written and verbal linguistic patterns of a particularuser. For example, the data analysis module 305 may determine aparticular linguistic written pattern of the user. This may includeidentifying the relationship between the words used to form a sentenceand determine written habits of the user. For example, a user may preferto use passive writing; another user may prefer use conjunctions. Infurther examples, a user may prefer to use shorthand. The data analysismodule 305 may identify these types of linguistic patterns.

In further embodiments, the data analysis module 305 may analyze theverbal linguistic patterns of a user. For example, the data analysismodule 305 may determine the spoken sentence structure patterns of auser. For example, the user may use proper English or Gregarianlanguage. A user may use more colloquialisms when speaking than whenwriting. The data analysis module 305 may identify all these patterns.

In some embodiments, the data analysis module 305 may identifycolloquialisms or slang words used in the verbal and written data. Insome instances, the colloquialisms or slang words may be generic. Inother instances, the colloquialisms and slang words may revealinformation about the user. For example, the data analysis module 305may determine if the slang words and colloquialisms identify an agegroup or geographical region associated with the user. For example, somecolloquialisms are inherent in a general geographical region (e.g.,sneakers versus tennis shoes, bubbler versus drinking fountain versuswater fountain). The data analysis module 305 may collect the variouscolloquialisms and use them to map a geographic region associated withthe user. In other embodiments, slang words may reveal information aboutthe user. For example, the data analysis module 305 may identify slangwords associated with a particular age group (e.g., lol, wassup, ducky,and how, peeps, etc.).

In some embodiments, the first communication module 115-a may containgeographical information relating to the location of a device or anautomation system. Therefore, the first communication module 115-a mayuse linguistic patterns associated with that geographical region.However, in some instances, a user may be from a different geographicalregion. In those instances, the data analysis module 305 may identify ageographical region where the user is from and adapt to the localjargon. For example, a user may be from the Northeast but may have movedto the Mountain West and installed an automation system at a home there.The first communication module 115-a may initially use Mountain Westjargon. However, the first communication module 115-a may adapt to usejargon of the Northeast to conform with the linguistic patterns of theuser based at least in part on the analysis performed by the dataanalysis module 305.

The linguistic algorithm module 310 may use the analysis generated bythe data analysis module 305 to create, adapt, update, or correct alinguistic algorithm. For example, the linguistic algorithm module 310may contain an algorithm for generating natural-language communications.The algorithm may be a generic algorithm that generates a genericnatural-language communication. In other embodiments, the algorithm maybe unique to the user. For example, the algorithm may adapt to thelinguistic pattern of the user. In some instances, the algorithm mayinclude colloquialisms preferred by the user. The linguistic algorithmmodule 310 may adapt to an age group associated with the user. Forexample, the data analysis module 305 may identify an age groupassociated with the user, the linguistic algorithm module 310 may thenincorporate other slang words and colloquialisms associated with thatage group.

The linguistic algorithm module 310 may also contain differentalgorithms based on the linguistic patterns of a user's written orverbal communications. For example, the data analysis module 305 mayidentify different patterns in a user's written and verbalcommunications. The differences may require different algorithms toadapt communications to their delivery method. For example, a writtenlinguistic algorithm may generate written communications and a verballinguistic algorithm may generate verbal communications.

In further embodiments, as discussed below, the linguistic algorithmmodule 310 may communicate with a linguistic algorithm module 410associated with a second communication module 250-a (See FIG. 4).

In some embodiments, the first communication module 115-a may furtherinclude the message module 315. The message module 315 may use thealgorithm in the linguistic algorithm module 310 to generate a message.The message may convey information to a user of an automation system.For example, the message may alert the user to an unauthorized entry toan automation system, that an automation system is armed/disarmed, thata door was left unlocked, that another user accessed the system, or thelike. The message may be a text message, ping message, email, voicemessage, phone call, or the like.

In some embodiments, the message may be a natural-languagecommunication. For example, the natural-language communication mayappear to be generated by a person rather than a computer. In someinstances, the user may easily interpret the natural-languagecommunication and its meaning. In some instances, the natural-languagecommunication may ease the anxiety a user experiences when interfacingwith an automation system. As the linguistic algorithm module 310advances, the messages generated by the message module 315 may be morerefined. Therefore, the user may eventually feel as though they aretalking to a friend rather than a computer. For example, thenatural-language communication may adapt to linguistic patterns of theuser.

FIG. 4 is a block diagram illustrating one example of the secondcommunication module 250-a. The second communication module 250-a may beone example of the second communication module 250 depicted in FIG. 2.As depicted, the second communication module 250-a may include a datacollection module 400, a data analysis module 305-a, a linguisticalgorithm module 410, and a message module 315-a. The secondcommunication module 250-a may include additional modules andcapabilities in other embodiments. Similarly, the second communicationmodule 250-a may include a few number of modules and functionalitiesthan that which is described with reference to FIG. 4.

The data collection module 400 may operate similar to data collectionmodule 300 (See FIG. 3). For example, data collection module 400 maycollect various written and verbal data associated with a user. However,since the data collection module 400 is remote from the user, a device(e.g. device 105) associated with a user may collect and transfer thedata to the data collection module 400. In some embodiments, the devicemay automatically send data to the data collection module 400. In otherembodiments, the data collection module 400 may request information fromthe device. The device may be any device associated with the user andthe data collection module 400 may communicate with a variety ofdevices. In some embodiments, the device and the data collection module400 may communicate over a network (e.g., network 210).

The data analysis module 305-a may be one example of a data analysismodule 305 depicted in FIG. 3. Therefore, data analysis module 305-a mayperform similar functions as data analysis module 305.

The linguistic algorithm module 410 may operate similar to linguisticalgorithm module 310 (See FIG. 3). For example, the linguistic algorithmmodule 410 may perform the same functions as the linguistic algorithmmodule 310. In some embodiments, the linguistic algorithm module 410 andthe linguistic algorithm module 310 may communicate. For example, thelinguistic algorithm module 410 may use the data analyzed by the dataanalysis module 305-a to adapt to a linguistic pattern of the user. Thelinguistic algorithm module 410 may send adapted algorithm to thelinguistic algorithm module 310 to ensure both modules 310, 410 areusing substantially the same algorithms. Similarly, updates made by thelinguistic algorithm module 310 may be sent to the linguistic algorithmmodule 410. In some embodiments, the linguistic algorithm module 410 mayping the linguistic algorithm module 310 for updates and vice versa.Therefore, in some embodiments, the natural-language communication mayappear the same regardless of whether the first or second communicationmodule 115-a, 250-b generated it.

The message module 315-a may be one example of the message module 315depicted in FIG. 3. Therefore, the message module 315-a may performsimilar functions as the message module 315.

FIG. 5 is a flow diagram illustrating one embodiment of a method 500 forpersonifying a natural-language communication. In some configurations,the method 500 may be implemented in whole or in part by the firstcommunication module 115 shown in FIGS. 1, 2 and/or 3. In furtherconfigurations, the method 500 may be implemented in whole or in part bythe second communication module 250 shown in FIGS. 2 and/or 4. In stillfurther embodiments, the method 500 may be performed generally by thedevice 105 shown in FIGS. 1 and/or 2, or even more generally by theenvironments 100, 200 shown in FIGS. 1 and/or 2.

At block 505, the linguistic pattern of a user may be observed. This mayinclude collecting verbal and written data indicative of a linguisticpattern of the user. The data may be collected via a device (e.g. device105). In some embodiments, the device may collect and store the data. Inother embodiments, the device may transfer the data to a service stationwhich may additionally or alternatively collect and store the data.

In further embodiments, the data may be collected via a survey. Forexample, at block 505, the user may be given a survey containingdifferent questions to assess the linguistic characteristics of theuser. The survey may be a written and/or verbal survey. It may includemultiple choice question, yes/no questions, open-ended questions, orsome combination of different types of questions. In some embodiments,the survey may set a baseline for adapting the natural-languagecommunications to a linguistic pattern of the user.

At block 510, the linguistic pattern of the user may be analyzed. Forexample, the different characteristics and individualisms of thelinguistic pattern may be observed and recorded. In some embodiments,the verbal and written data may be differentiated and analyzedseparately. In other embodiments, the written data and verbal data maybe analyzed concurrently.

At block 515, a natural-language communication may be adapted based atleast in part on the analyzed linguistic pattern of the user. This mayinclude adapting the natural-language communication to use words orphrases favored by the user. The natural-language communication may alsoadapt to a preferred sentence structure or syntax of the user. In someembodiments, the natural-language communication may adapt to use slangwords or colloquialisms associated with an age group and/or ageographical region associated with the user. The natural-languagecommunication may differ for verbal and written data, or in someinstances, may be the same for both verbal and written communications.

FIG. 6 is a flow diagram illustrating one embodiment of a method 600 topersonify a natural-language communication. In some configurations, themethod 600 may be implemented in whole or in part by the firstcommunication module 115 depicted in FIGS. 1, 2, and/or 3. In otherembodiments, the method 600 may be implemented in whole or in part bythe second communication module 250 depicted in FIGS. 2 and/or 4. Insome configurations, the method 600 may be implemented in whole or inpart with the method 500 depicted in FIG. 5.

At block 605, data may be gathered relating to a linguistic pattern ofthe user. The data may be gathered by a device (e.g. device 105) orconversely may be gathered by a service station (e.g. service station215). At block 610, it may be determined whether the data is writtendata or verbal data.

If the data is written data, at block 615, a preferred sentencestructure may be identified. For example, the sentence structure of theuser may be analyzed to determine the type of sentence structure theuser prefers. For instance, a user may prefer to use one of a simplesentence, complex sentence, compound sentence, special compoundsentence, or the like. In some instances, a user may prefer to use acombination of sentence structures.

At block 620, preferred words or phrases may be identified. For example,a series of colloquialisms or slang words preferred by the user may beidentified. In some instances, the relationship between the words orphrases and structure of the words or phrases may also be identified.For example, a user may prefer to write-out numbers (e.g. 5 versusfive). A user may also use emoticons or internet slang (e.g. :o) or lol,rofl). In other embodiments, a user may prefer conjunctions. Other typesof linguistic patterns may also be identified. In some embodiments, atblock 620, the preferred words or phrases may categorize the individualuser. For example, the preferred words or phrases may identify ageographical region, dialect, or age group associated with the user.

At block 615, the written data analysis may be compared to a verbal dataanalysis (if available). For example, at block 615, the sentencestructures of the verbal and written data, as well as preferred wordsand phrases, may be compared. At block 630, it may be determined if thedata shows similar patterns. If the comparison does not shown similarpatterns, at block 640, a written linguistic algorithm may be updated.If the data is similar, the method 600 may continue to block 635. Atblock 635, an overall linguistic algorithm may be updated. The overalllinguistic algorithm may generate both written and verbalcommunications.

If, at block 610, it is determined the data is verbal data, the method600 may continue to block 645. At block 645, a preferred sentencestructure may be identified. Similar to block 615, the sentencestructure of the user may be analyzed to determine the type of sentencestructure the user prefers. For instance, a user may prefer to use oneof a simple sentence, complex sentence, compound sentence, specialcompound sentence, or the like. In some instances, a user may prefer touse a combination of sentence structures.

At block 650, preferred words or phrases may be identified. Similar toblock 620, a series of colloquialisms or slang words preferred by theuser may be identified. In some instances, the relationship between thewords or phrases and structure of the words or phrases may also beidentified. For example, a user may use slang or may speak in internetslang (e.g. don't know versus dunno, y'all, or saying LOL, ROFL, etc.).In some embodiments, at block 620, the preferred words or phrases maycategorize the individual user. For example, the preferred words orphrases may identify a geographical region, dialect, or age groupassociated with the user.

At block 655, the verbal data analysis may be compared to the writtendata analysis (if the written data analysis is available). At block 660,it may be determined if the verbal data and written data have similarpatterns. If similar patterns exist, at block 635, an overall linguisticalgorithm may be updated. If similar patterns do not exist, at block665, a verbal linguistic algorithm may be updated. The verbal linguisticalgorithm may generate verbal messages.

In some embodiments, the verbal, written, or overall linguisticalgorithms (referred to generally in this paragraph as “algorithm”) maybe updated at various time intervals. For example, the algorithm may beupdated at set time intervals. For example, it may be updatedapproximately every half-hour, hourly, bi-daily, daily, weekly, monthly,etc. In some embodiments, the algorithm may be updated more frequentlyfor a new user and less frequently for an older user. For example, for anew user, the algorithm may be updated daily, whereas an older user'salgorithm may be updated monthly. As the new user's algorithm becomesmore personalized, the algorithm may reduce the frequency of updates.For example, the algorithm may first update every bi-daily, then daily,then bi-weekly, then weekly, etc. As the algorithm is refined, it mayupdate less often.

In other embodiments, the algorithm may not be updated on a set timetable, but rather based upon the amount of information observed andanalyzed. For example, the method 500 may have a predetermined amount ofdata to collect before updating the algorithm to ensure a propersampling size. In some instances, this may include approximately fivedifferent pieces of data (e.g., two emails, one text message, two voicecommands, and one voice message). In other embodiments, it may includemore or less. As with a set time table updating system, using a dataset, the algorithm may update more frequently at first then expand outto update after a larger sampling size is collected.

FIG. 7 depicts a block diagram of a controller 700 suitable forimplementing the present systems and methods. The controller 700 may bean example of the device 105 illustrated in FIGS. 1 and/or 2. In oneconfiguration, the controller 700 may include a bus 705 whichinterconnects major subsystems of controller 700, such as a centralprocessor 710, a system memory 715 (typically RAM, but which may alsoinclude ROM, flash RAM, or the like), an input/output controller 720, anexternal audio device, such as a speaker system 725 via an audio outputinterface 730, an external device, such as a display screen 735 viadisplay adapter 740, an input device 745 (e.g., remote control deviceinterfaced with an input controller 750), multiple USB devices 765(interfaced with a USB controller 770), one or more cellular radios 790,and a storage interface 780. Also included are at least one sensor 755connected to bus 705 through a sensor controller 760 and a networkinterface 785 (coupled directly to bus 705).

Bus 705 may allow data communication between central processor 710 andsystem memory 715, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM is generally the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components or devices. For example, first communicationmodule 115-b to implement the present systems and methods may be storedwithin the system memory 715. The first communication module 115-b maybe an example of the first communication module 115 illustrated in FIGS.1, 2, and/or 3. Applications (e.g., application 225) resident withcontroller 700 are generally stored on and accessed via a non-transitorycomputer readable medium, such as a hard disk drive (e.g., fixed disk775) or other storage medium. Additionally, applications can be in theform of electronic signals modulated in accordance with the applicationand data communication technology when accessed via network interface785.

Storage interface 780, as with the other storage interfaces ofcontroller 700, can connect to a standard computer readable medium forstorage and/or retrieval of information, such as a fixed disk drive 775.Fixed disk drive 775 may be a part of controller 700 or may be separateand accessed through other interface systems. Network interface 785 mayprovide a direct connection to a remote server via a direct network linkto the Internet via a POP (point of presence). Network interface 785 mayprovide such connection using wireless techniques, including digitalcellular telephone connection, Cellular Digital Packet Data (CDPD)connection, digital satellite data connection, or the like. In someembodiments, one or more sensors (e.g., motion sensor, smoke sensor,glass break sensor, door sensor, window sensor, carbon monoxide sensor,and the like) connect to controller 700 wirelessly via network interface785. In one configuration, the cellular radio 790 may include a receiverand transmitter to wirelessly receive and transmit communications via,for example, a cellular network. The cellular radio 790 may be used totransmit information to the service station 215 via the network 210.

Many other devices or subsystems (not shown) may be connected in asimilar manner (e.g., entertainment system, computing device, remotecameras, wireless key fob, wall mounted user interface device, cellradio module, battery, alarm siren, door lock, lighting system,thermostat, home appliance monitor, utility equipment monitor, and soon). Conversely, all of the devices shown in FIG. 7 need not be presentto practice the present systems and methods. The devices and subsystemscan be interconnected in different ways from that shown in FIG. 7. Theaspect of some operations of a system such as that shown in FIG. 7 arereadily known in the art and are not discussed in detail in thisapplication. Code to implement the present disclosure can be stored in anon-transitory computer-readable medium such as one or more of systemmemory 715 or fixed disk 775. The operating system provided oncontroller 700 may be iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®,UNIX®, LINUX®, or another known operating system.

Moreover, regarding the signals described herein, those skilled in theart will recognize that a signal can be directly transmitted from afirst block to a second block, or a signal can be modified (e.g.,amplified, attenuated, delayed, latched, buffered, inverted, filtered,or otherwise modified) between the blocks. Although the signals of theabove described embodiment are characterized as transmitted from oneblock to the next, other embodiments of the present systems and methodsmay include modified signals in place of such directly transmittedsignals as long as the informational and/or functional aspect of thesignal is transmitted between blocks. To some extent, a signal input ata second block can be conceptualized as a second signal derived from afirst signal output from a first block due to physical limitations ofthe circuitry involved (e.g., there will inevitably be some attenuationand delay). Therefore, as used herein, a second signal derived from afirst signal includes the first signal or any modifications to the firstsignal, whether due to circuit limitations or due to passage throughother circuit elements which do not change the informational and/orfinal functional aspect of the first signal.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexemplary in nature since many other architectures can be implemented toachieve the same functionality.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various exemplary methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

Furthermore, while various embodiments have been described and/orillustrated herein in the context of fully functional computing systems,one or more of these exemplary embodiments may be distributed as aprogram product in a variety of forms, regardless of the particular typeof computer-readable media used to actually carry out the distribution.The embodiments disclosed herein may also be implemented using softwaremodules that perform certain tasks. These software modules may includescript, batch, or other executable files that may be stored on acomputer-readable storage medium or in a computing system. In someembodiments, these software modules may configure a computing system toperform one or more of the exemplary embodiments disclosed herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the present systems and methods and their practicalapplications, to thereby enable others skilled in the art to bestutilize the present systems and methods and various embodiments withvarious modifications as may be suited to the particular usecontemplated.

Unless otherwise noted, the terms “a” or “an,” as used in thespecification and claims, are to be construed as meaning “at least oneof” In addition, for ease of use, the words “including” and “having,” asused in the specification and claims, are interchangeable with and havethe same meaning as the word “comprising.” In addition, the term “basedon” as used in the specification and the claims is to be construed asmeaning “based at least upon.”

What is claimed is:
 1. A method for generating a natural-languagecommunication message at an automation system of a premises, comprising:receiving, at the automation system, a communication from a user;determining, by the automation system and based at least in part onreceiving the communication, an aggregate linguistic pattern of the userthat is based at least in part on a comparison of an oral linguisticpattern of the user with a written linguistic pattern of the user;identifying system information to communicate to the user, wherein thesystem information is associated with a status of the automation systemin relation to the premises; and generating, at the automation system,the natural-language communication message based at least in part on theaggregate linguistic pattern of the user and the identified systeminformation, the natural-language communication message comprising anotification of the identified system information.
 2. The method ofclaim 1, further comprising: identifying one or more words or phrases ofthe user based at least in part on receiving the communication, whereindetermining the aggregate linguistic pattern of the user is based atleast in part on identifying the one or more words or phrases.
 3. Themethod of claim 2, further comprising: identifying a geographical regionassociated with the one or more words or phrases of the user, whereinthe natural-language communication message is based at least in part onthe geographical region associated with the one or more words or phrasesof the user.
 4. The method of claim 1, wherein the communication fromthe user comprises an oral communication from the user, a writtencommunication from the user, or both.
 5. The method of claim 4, whereindetermining the aggregate linguistic pattern of the user comprisesdetermining a relationship between one or more spoken words of the oralcommunication, one or more written words of the written communication,or both.
 6. The method of claim 4, wherein the communication from theuser comprises both the oral communication and the writtencommunication, the method further comprising: comparing a sentencestructure of the oral communication from the user with a sentencestructure of the written communication from the user, whereindetermining the aggregate linguistic pattern of the user is based atleast in part on comparing the sentence structure of the oralcommunication from the user with the sentence structure of the writtencommunication from the user.
 7. The method of claim 1, furthercomprising: receiving a second communication from the user, wherein theaggregate linguistic pattern of the user is updated based at least inpart on receiving the second communication, and wherein a secondnatural-language communication message is generated based at least inpart on updating the aggregate linguistic pattern of the user.
 8. Themethod of claim 1, wherein determining the aggregate linguistic patternof the user comprises determining a preferred sentence structure of theuser, wherein the natural-language communication message comprises thepreferred sentence structure of the user.
 9. An apparatus for generatinga natural-language communication message at an automation system of apremises, comprising: a processor; a memory in electronic communicationwith the processor; and instructions stored in the memory, theinstructions being executable by the processor to: receive acommunication from a user; determine, based at least in part onreceiving the communication, an aggregate linguistic pattern of the userthat is based at least in part on a comparison of an oral linguisticpattern of the user with a written linguistic pattern of the user;identify system information to communicate to the user, wherein thesystem information is associated with a status of the automation systemin relation to the premises; and generate the natural-languagecommunication message based at least in part on the aggregate linguisticpattern of the user and the identified system information, thenatural-language communication message comprising a notification of theidentified system information.
 10. The apparatus of claim 9, wherein theinstructions are further executable to instruct the processor to:identify one or more words or phrases of the user based at least in parton receiving the communication, wherein determining the aggregatelinguistic pattern of the user is based at least in part on identifyingthe one or more words or phrases.
 11. The apparatus of claim 10, whereinthe instructions are further executable to instruct the processor to:identify a geographical region associated with the one or more words orphrases of the user, wherein the natural-language communication messageis based at least in part on the geographical region associated with theone or more words or phrases of the user.
 12. The apparatus of claim 9,wherein the communication from the user comprises an oral communicationfrom the user, a written communication from the user, or both.
 13. Theapparatus of claim 12, wherein determining the aggregate linguisticpattern of the user comprises determining a relationship between one ormore spoken words of the oral communication, one or more written wordsof the written communication, or both.
 14. The apparatus of claim 12,wherein the communication from the user comprises both the oralcommunication and the written communication, and wherein theinstructions are further executable to instruct the processor to:compare a sentence structure of the oral communication from the userwith a sentence structure of the written communication from the user,wherein determining the aggregate linguistic pattern of the user isbased at least in part on comparing the sentence structure of the oralcommunication from the user with the sentence structure of the writtencommunication from the user.
 15. The apparatus of claim 9, wherein theinstructions are further executable to instruct the processor to:receive a second communication from the user, wherein the aggregatelinguistic pattern of the user is updated based at least in part onreceiving the second communication, and wherein a secondnatural-language communication message is generated based at least inpart on updating the aggregate linguistic pattern of the user.
 16. Theapparatus of claim 9, wherein determining the aggregate linguisticpattern of the user comprises determining a preferred sentence structureof the user, wherein the natural-language communication messagecomprises the preferred sentence structure of the user.
 17. Acomputer-program product for generating a natural-language communicationmessage at an automation system of a premises, the computer-programproduct comprising a non-transitory computer-readable medium storinginstructions executable by a processor to: receive a communication froma user; determine, based at least in part on receiving thecommunication, an aggregate linguistic pattern of the user that is basedat least in part on a comparison of an oral linguistic pattern of theuser with a written linguistic pattern of the user; identify systeminformation to communicate to the user, wherein the system informationis associated with a status of the automation system in relation to thepremises; and generate the natural-language communication message basedat least in part on the aggregate linguistic pattern of the user and theidentified system information, the natural-language communicationmessage comprising a notification of the identified system information.18. The computer-program product of claim 17, wherein the instructionsare further executable by the processor to: identify one or more wordsor phrases of the user based at least in part on receiving thecommunication, wherein determining the aggregate linguistic pattern ofthe user is based at least in part on identifying the one or more wordsor phrases.
 19. The computer-program product of claim 18, wherein theinstructions are further executable by the processor to: identify ageographical region associated with the one or more words or phrases ofthe user, wherein the natural-language communication message is based atleast in part on the geographical region associated with the one or morewords or phrases of the user.
 20. The computer-program product of claim17, wherein the communication from the user comprises an oralcommunication from the user, a written communication from the user, orboth.